o tBh@sLddlZddlmZddlmZddlmZddl m Z ddZ d d Z dS) N)sparse)suppress) is_scalar_nan)_object_dtype_isnancCstttddl}||jur||WdSWdn1s%wYt|rQ|jjdkr;t |}|S|jjdvrKt j |j t d}|St|}|S||k}|S)Nrf)iu)dtype)r ImportErrorAttributeErrorpandasNAisnarr kindnpisnanzerosshapeboolr)X value_to_maskr Xtrj/var/www/html/riverr-enterprise-integrations-main/venv/lib/python3.10/site-packages/sklearn/utils/_mask.py_get_dense_mask s$     rcCs\t|s t||St|j|}|jdkrtjntj}|||j|j f|j t d}|S)aCompute the boolean mask X == value_to_mask. Parameters ---------- X : {ndarray, sparse matrix} of shape (n_samples, n_features) Input data, where ``n_samples`` is the number of samples and ``n_features`` is the number of features. value_to_mask : {int, float} The value which is to be masked in X. Returns ------- X_mask : {ndarray, sparse matrix} of shape (n_samples, n_features) Missing mask. csr)rr ) spissparserdataformat csr_matrix csc_matrixindicescopyindptrrr)rrrsparse_constructor Xt_sparserrr _get_mask!s   r() numpyrscipyrr contextlibrrfixesrrr(rrrrs